Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations149010
Missing cells74
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 MiB
Average record size in memory96.0 B

Variable types

Text2
Numeric8
Categorical2

Alerts

crawling_date has constant value "20241130" Constant
avg_rating is highly overall correlated with num_of_ratings and 1 other fieldsHigh correlation
num_of_enrolled is highly overall correlated with num_of_ratings and 2 other fieldsHigh correlation
num_of_ratings is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_reviews is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_top_instructor_courses is highly overall correlated with num_of_top_instructor_leanersHigh correlation
num_of_top_instructor_leaners is highly overall correlated with num_of_enrolled and 3 other fieldsHigh correlation
Rating is highly imbalanced (62.4%) Imbalance
helpfulness is highly skewed (γ1 = 44.41021962) Skewed
helpfulness has 143141 (96.1%) zeros Zeros

Reproduction

Analysis started2025-01-14 13:53:22.358392
Analysis finished2025-01-14 13:53:34.002490
Duration11.64 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:34.223451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length79
Median length56
Mean length21.456265
Min length2

Characters and Unicode

Total characters3197198
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfoundations-of-cybersecurity
2nd rowfoundations-of-cybersecurity
3rd rowfoundations-of-cybersecurity
4th rowfoundations-of-cybersecurity
5th rowfoundations-of-cybersecurity
ValueCountFrequency (%)
foundations-user-experience-design 9969
 
6.7%
python 9965
 
6.7%
python-data 9964
 
6.7%
python-network-data 7094
 
4.8%
html 6583
 
4.4%
foundations-of-cybersecurity 5492
 
3.7%
html-css-javascript-for-web-developers 4123
 
2.8%
matlab 4010
 
2.7%
introduction-tensorflow 3800
 
2.6%
python-basics 3779
 
2.5%
Other values (195) 84231
56.5%
2025-01-14T22:53:34.556795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 295263
 
9.2%
e 272013
 
8.5%
t 269558
 
8.4%
o 251923
 
7.9%
n 240195
 
7.5%
a 213816
 
6.7%
r 204506
 
6.4%
i 200749
 
6.3%
s 179616
 
5.6%
d 128571
 
4.0%
Other values (20) 940988
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3197198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 295263
 
9.2%
e 272013
 
8.5%
t 269558
 
8.4%
o 251923
 
7.9%
n 240195
 
7.5%
a 213816
 
6.7%
r 204506
 
6.4%
i 200749
 
6.3%
s 179616
 
5.6%
d 128571
 
4.0%
Other values (20) 940988
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3197198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 295263
 
9.2%
e 272013
 
8.5%
t 269558
 
8.4%
o 251923
 
7.9%
n 240195
 
7.5%
a 213816
 
6.7%
r 204506
 
6.4%
i 200749
 
6.3%
s 179616
 
5.6%
d 128571
 
4.0%
Other values (20) 940988
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3197198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 295263
 
9.2%
e 272013
 
8.5%
t 269558
 
8.4%
o 251923
 
7.9%
n 240195
 
7.5%
a 213816
 
6.7%
r 204506
 
6.4%
i 200749
 
6.3%
s 179616
 
5.6%
d 128571
 
4.0%
Other values (20) 940988
29.4%

posted_date
Real number (ℝ)

Distinct3382
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20207571
Minimum20150813
Maximum20241201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:34.672314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum20150813
5-th percentile20170124
Q120200422
median20201212
Q320221115
95-th percentile20240613
Maximum20241201
Range90388
Interquartile range (IQR)20693

Descriptive statistics

Standard deviation21029.923
Coefficient of variation (CV)0.0010406952
Kurtosis-0.21332427
Mean20207571
Median Absolute Deviation (MAD)18906
Skewness-0.40985919
Sum3.0111302 × 1012
Variance4.4225766 × 108
MonotonicityNot monotonic
2025-01-14T22:53:34.771776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200531 292
 
0.2%
20200530 282
 
0.2%
20200601 281
 
0.2%
20200525 273
 
0.2%
20200518 273
 
0.2%
20200511 273
 
0.2%
20200512 271
 
0.2%
20200713 269
 
0.2%
20200516 267
 
0.2%
20200608 266
 
0.2%
Other values (3372) 146263
98.2%
ValueCountFrequency (%)
20150813 1
< 0.1%
20150815 2
< 0.1%
20150818 1
< 0.1%
20150819 1
< 0.1%
20150820 1
< 0.1%
20150826 1
< 0.1%
20150827 1
< 0.1%
20150828 1
< 0.1%
20150831 2
< 0.1%
20150903 1
< 0.1%
ValueCountFrequency (%)
20241201 1
 
< 0.1%
20241130 22
< 0.1%
20241129 31
< 0.1%
20241128 34
< 0.1%
20241127 27
< 0.1%
20241126 34
< 0.1%
20241125 45
< 0.1%
20241124 41
< 0.1%
20241123 42
< 0.1%
20241122 42
< 0.1%

crawling_date
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
20241130
149010 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1192080
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20241130
2nd row20241130
3rd row20241130
4th row20241130
5th row20241130

Common Values

ValueCountFrequency (%)
20241130 149010
100.0%

Length

2025-01-14T22:53:34.864806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T22:53:34.953379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
20241130 149010
100.0%

Most occurring characters

ValueCountFrequency (%)
2 298020
25.0%
0 298020
25.0%
1 298020
25.0%
4 149010
12.5%
3 149010
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1192080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 298020
25.0%
0 298020
25.0%
1 298020
25.0%
4 149010
12.5%
3 149010
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1192080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 298020
25.0%
0 298020
25.0%
1 298020
25.0%
4 149010
12.5%
3 149010
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1192080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 298020
25.0%
0 298020
25.0%
1 298020
25.0%
4 149010
12.5%
3 149010
12.5%

Rating
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
5
123434 
4
18289 
3
 
4580
2
 
1467
1
 
1240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters149010
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Length

2025-01-14T22:53:35.029049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T22:53:35.124695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 123434
82.8%
4 18289
 
12.3%
3 4580
 
3.1%
2 1467
 
1.0%
1 1240
 
0.8%

avg_rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7422797
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:35.204350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.6
Q14.7
median4.8
Q34.8
95-th percentile4.9
Maximum4.9
Range1.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10447842
Coefficient of variation (CV)0.022031265
Kurtosis4.1711331
Mean4.7422797
Median Absolute Deviation (MAD)0.1
Skewness-1.339126
Sum706647.1
Variance0.010915741
MonotonicityNot monotonic
2025-01-14T22:53:35.285773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.8 70554
47.3%
4.7 40441
27.1%
4.6 18590
 
12.5%
4.9 13362
 
9.0%
4.5 3808
 
2.6%
4.4 1444
 
1.0%
4.3 624
 
0.4%
4.1 68
 
< 0.1%
4 44
 
< 0.1%
3.9 40
 
< 0.1%
Other values (2) 35
 
< 0.1%
ValueCountFrequency (%)
3.8 8
 
< 0.1%
3.9 40
 
< 0.1%
4 44
 
< 0.1%
4.1 68
 
< 0.1%
4.2 27
 
< 0.1%
4.3 624
 
0.4%
4.4 1444
 
1.0%
4.5 3808
 
2.6%
4.6 18590
12.5%
4.7 40441
27.1%
ValueCountFrequency (%)
4.9 13362
 
9.0%
4.8 70554
47.3%
4.7 40441
27.1%
4.6 18590
 
12.5%
4.5 3808
 
2.6%
4.4 1444
 
1.0%
4.3 624
 
0.4%
4.2 27
 
< 0.1%
4.1 68
 
< 0.1%
4 44
 
< 0.1%

num_of_ratings
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36113.824
Minimum13
Maximum229752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:35.375939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile1170
Q13661
median13222
Q327905
95-th percentile229752
Maximum229752
Range229739
Interquartile range (IQR)24244

Descriptive statistics

Standard deviation58225.838
Coefficient of variation (CV)1.6122867
Kurtosis5.3098371
Mean36113.824
Median Absolute Deviation (MAD)10265
Skewness2.45854
Sum5.381321 × 109
Variance3.3902482 × 109
MonotonicityNot monotonic
2025-01-14T22:53:35.480418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69498 9969
 
6.7%
229752 9965
 
6.7%
96077 9964
 
6.7%
44178 7094
 
4.8%
27564 6583
 
4.4%
27905 5492
 
3.7%
16672 4123
 
2.8%
17700 4010
 
2.7%
19521 3800
 
2.6%
17773 3779
 
2.5%
Other values (183) 84231
56.5%
ValueCountFrequency (%)
13 1
 
< 0.1%
19 6
 
< 0.1%
24 24
< 0.1%
28 8
 
< 0.1%
30 9
 
< 0.1%
39 18
< 0.1%
41 10
< 0.1%
42 13
< 0.1%
52 11
< 0.1%
55 11
< 0.1%
ValueCountFrequency (%)
229752 9965
6.7%
96077 9964
6.7%
69498 9969
6.7%
44178 7094
4.8%
27905 5492
3.7%
27564 6583
4.4%
21320 2691
 
1.8%
19521 3800
 
2.6%
17773 3779
 
2.5%
17700 4010
2.7%

helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15433192
Minimum0
Maximum239
Zeros143141
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:35.580937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0058198
Coefficient of variation (CV)12.996791
Kurtosis3241.1155
Mean0.15433192
Median Absolute Deviation (MAD)0
Skewness44.41022
Sum22997
Variance4.0233129
MonotonicityNot monotonic
2025-01-14T22:53:35.775115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143141
96.1%
1 3306
 
2.2%
2 809
 
0.5%
3 388
 
0.3%
4 282
 
0.2%
5 184
 
0.1%
6 141
 
0.1%
7 97
 
0.1%
8 89
 
0.1%
9 82
 
0.1%
Other values (68) 491
 
0.3%
ValueCountFrequency (%)
0 143141
96.1%
1 3306
 
2.2%
2 809
 
0.5%
3 388
 
0.3%
4 282
 
0.2%
5 184
 
0.1%
6 141
 
0.1%
7 97
 
0.1%
8 89
 
0.1%
9 82
 
0.1%
ValueCountFrequency (%)
239 1
< 0.1%
202 1
< 0.1%
144 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
108 2
< 0.1%
99 2
< 0.1%
Distinct125712
Distinct (%)84.4%
Missing74
Missing (%)< 0.1%
Memory size1.1 MiB
2025-01-14T22:53:36.074230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6153
Median length2287
Mean length112.88996
Min length1

Characters and Unicode

Total characters16813379
Distinct characters146
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122712 ?
Unique (%)82.4%

Sample

1st rowThe course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field
2nd rowInformation was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.
3rd rowFor a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.
4th rowI think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!
5th rowSurprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.
ValueCountFrequency (%)
the 128039
 
4.5%
course 97382
 
3.4%
and 94042
 
3.3%
to 93213
 
3.3%
a 66977
 
2.4%
i 64719
 
2.3%
of 51245
 
1.8%
this 48652
 
1.7%
is 45538
 
1.6%
for 43044
 
1.5%
Other values (56150) 2113697
74.3%
2025-01-14T22:53:36.488013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2709398
16.1%
e 1665893
 
9.9%
t 1115271
 
6.6%
o 1085929
 
6.5%
a 984816
 
5.9%
n 938406
 
5.6%
r 895340
 
5.3%
s 881626
 
5.2%
i 854155
 
5.1%
l 573398
 
3.4%
Other values (136) 5109147
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16813379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2709398
16.1%
e 1665893
 
9.9%
t 1115271
 
6.6%
o 1085929
 
6.5%
a 984816
 
5.9%
n 938406
 
5.6%
r 895340
 
5.3%
s 881626
 
5.2%
i 854155
 
5.1%
l 573398
 
3.4%
Other values (136) 5109147
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16813379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2709398
16.1%
e 1665893
 
9.9%
t 1115271
 
6.6%
o 1085929
 
6.5%
a 984816
 
5.9%
n 938406
 
5.6%
r 895340
 
5.3%
s 881626
 
5.2%
i 854155
 
5.1%
l 573398
 
3.4%
Other values (136) 5109147
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16813379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2709398
16.1%
e 1665893
 
9.9%
t 1115271
 
6.6%
o 1085929
 
6.5%
a 984816
 
5.9%
n 938406
 
5.6%
r 895340
 
5.3%
s 881626
 
5.2%
i 854155
 
5.1%
l 573398
 
3.4%
Other values (136) 5109147
30.4%

num_of_reviews
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3920.0919
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:36.598405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile208
Q1836
median2698
Q36623
95-th percentile10000
Maximum10000
Range9999
Interquartile range (IQR)5787

Descriptive statistics

Standard deviation3604.5532
Coefficient of variation (CV)0.91950733
Kurtosis-1.0444175
Mean3920.0919
Median Absolute Deviation (MAD)2112
Skewness0.70156583
Sum5.841329 × 108
Variance12992804
MonotonicityNot monotonic
2025-01-14T22:53:36.703184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 29898
 
20.1%
7113 7094
 
4.8%
6623 6583
 
4.4%
5510 5492
 
3.7%
4131 4123
 
2.8%
4032 4010
 
2.7%
3809 3800
 
2.6%
3784 3779
 
2.5%
2854 2851
 
1.9%
2779 2748
 
1.8%
Other values (168) 78632
52.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 7
 
< 0.1%
8 40
< 0.1%
9 27
< 0.1%
10 30
< 0.1%
11 22
< 0.1%
12 12
 
< 0.1%
ValueCountFrequency (%)
10000 29898
20.1%
7113 7094
 
4.8%
6623 6583
 
4.4%
5510 5492
 
3.7%
4131 4123
 
2.8%
4032 4010
 
2.7%
3809 3800
 
2.6%
3784 3779
 
2.5%
2854 2851
 
1.9%
2779 2748
 
1.8%

num_of_enrolled
Real number (ℝ)

High correlation 

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676194.78
Minimum1507
Maximum3205753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:36.808938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1507
5-th percentile65412
Q1182631
median408135
Q3988223
95-th percentile3205753
Maximum3205753
Range3204246
Interquartile range (IQR)805592

Descriptive statistics

Standard deviation777814.48
Coefficient of variation (CV)1.1502817
Kurtosis4.6242505
Mean676194.78
Median Absolute Deviation (MAD)261381
Skewness2.22966
Sum1.0075978 × 1011
Variance6.0499537 × 1011
MonotonicityNot monotonic
2025-01-14T22:53:36.910495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361423 9969
 
6.7%
3205753 9965
 
6.7%
1076350 9964
 
6.7%
682235 7094
 
4.8%
574559 6583
 
4.4%
988223 5492
 
3.7%
1171384 4123
 
2.8%
497579 4010
 
2.7%
385916 3800
 
2.6%
459997 3779
 
2.5%
Other values (195) 84231
56.5%
ValueCountFrequency (%)
1507 8
< 0.1%
2165 11
< 0.1%
2233 6
< 0.1%
2240 13
< 0.1%
2429 7
< 0.1%
3736 8
< 0.1%
3802 1
 
< 0.1%
4033 14
< 0.1%
5198 12
< 0.1%
5932 10
< 0.1%
ValueCountFrequency (%)
3205753 9965
6.7%
1361423 9969
6.7%
1171384 4123
2.8%
1076350 9964
6.7%
988223 5492
3.7%
682235 7094
4.8%
582205 2712
 
1.8%
574559 6583
4.4%
540079 2678
 
1.8%
535924 2259
 
1.5%

num_of_top_instructor_courses
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.87373
Minimum2
Maximum1675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:37.005239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median60
Q3129
95-th percentile325
Maximum1675
Range1673
Interquartile range (IQR)120

Descriptive statistics

Standard deviation218.42102
Coefficient of variation (CV)1.9350916
Kurtosis33.342219
Mean112.87373
Median Absolute Deviation (MAD)51
Skewness5.1733414
Sum16819315
Variance47707.74
MonotonicityNot monotonic
2025-01-14T22:53:37.090409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
60 40114
26.9%
325 27680
18.6%
9 11667
 
7.8%
4 9246
 
6.2%
129 8171
 
5.5%
5 6642
 
4.5%
18 6518
 
4.4%
2 5113
 
3.4%
8 5100
 
3.4%
12 4900
 
3.3%
Other values (19) 23859
16.0%
ValueCountFrequency (%)
2 5113
3.4%
3 849
 
0.6%
4 9246
6.2%
5 6642
4.5%
6 1575
 
1.1%
7 2993
 
2.0%
8 5100
3.4%
9 11667
7.8%
10 50
 
< 0.1%
12 4900
3.3%
ValueCountFrequency (%)
1675 2059
 
1.4%
325 27680
18.6%
196 92
 
0.1%
129 8171
 
5.5%
87 177
 
0.1%
66 20
 
< 0.1%
60 40114
26.9%
58 2542
 
1.7%
53 1525
 
1.0%
48 12
 
< 0.1%

num_of_top_instructor_leaners
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635335.4
Minimum3945
Maximum11153139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-14T22:53:37.193701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3945
5-th percentile167950
Q1517137
median1245385
Q34384601
95-th percentile11153139
Maximum11153139
Range11149194
Interquartile range (IQR)3867464

Descriptive statistics

Standard deviation3938852.4
Coefficient of variation (CV)1.0834908
Kurtosis-0.33852548
Mean3635335.4
Median Absolute Deviation (MAD)1124756
Skewness1.0469686
Sum5.4170133 × 1011
Variance1.5514558 × 1013
MonotonicityNot monotonic
2025-01-14T22:53:37.298318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4384601 40114
26.9%
11153139 27680
18.6%
954353 8171
 
5.5%
1071441 6272
 
4.2%
517137 4583
 
3.1%
1245385 4494
 
3.0%
596976 4491
 
3.0%
506004 4124
 
2.8%
426823 3931
 
2.6%
528545 3887
 
2.6%
Other values (73) 41263
27.7%
ValueCountFrequency (%)
3945 8
 
< 0.1%
6855 7
 
< 0.1%
7841 22
< 0.1%
8506 44
< 0.1%
8682 8
 
< 0.1%
8703 10
 
< 0.1%
18193 24
< 0.1%
19837 20
< 0.1%
21526 45
< 0.1%
29959 45
< 0.1%
ValueCountFrequency (%)
11153139 27680
18.6%
4384601 40114
26.9%
3081791 1525
 
1.0%
2767486 2059
 
1.4%
1245385 4494
 
3.0%
1071441 6272
 
4.2%
1065753 92
 
0.1%
1014757 2542
 
1.7%
954353 8171
 
5.5%
892947 1
 
< 0.1%

Interactions

2025-01-14T22:53:32.635341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:26.992380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.826949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.611063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.448604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.229663image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.013128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.823334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.819664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.089737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.920473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.706585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.547124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.327318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.115798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.922994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.918202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.194389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.013194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.876112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.644744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.424629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.220336image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.026845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:33.017897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.291866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.103820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.961626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.740498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.515369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.315924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.123565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:33.117582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.422393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.208291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.056147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.832050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.612050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.413670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.229072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:33.216242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.523930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.307772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.149834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.925759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.706384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.511352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.328656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:33.316795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.623734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.410297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.248307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.025392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.810904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.611023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.433303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:33.422374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:27.727478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:28.511816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:29.351871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.124933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:30.911425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:31.717688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:53:32.533821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-14T22:53:37.383045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Ratingavg_ratinghelpfulnessnum_of_enrollednum_of_ratingsnum_of_reviewsnum_of_top_instructor_coursesnum_of_top_instructor_leanersposted_date
Rating1.0000.1180.0460.1130.1140.1200.0720.0930.033
avg_rating0.1181.000-0.1150.4920.5720.5300.3860.485-0.004
helpfulness0.046-0.1151.000-0.123-0.141-0.139-0.072-0.098-0.021
num_of_enrolled0.1130.492-0.1231.0000.9380.9430.3520.635-0.126
num_of_ratings0.1140.572-0.1410.9381.0000.9840.3840.606-0.215
num_of_reviews0.1200.530-0.1390.9430.9841.0000.3410.569-0.201
num_of_top_instructor_courses0.0720.386-0.0720.3520.3840.3411.0000.8510.323
num_of_top_instructor_leaners0.0930.485-0.0980.6350.6060.5690.8511.0000.187
posted_date0.033-0.004-0.021-0.126-0.215-0.2010.3230.1871.000

Missing values

2025-01-14T22:53:33.557002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-14T22:53:33.774500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

course_nameposted_datecrawling_dateRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leaners
0foundations-of-cybersecurity202307292024113054.82790554The course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field. It is very good for the beginners who are new to this field551098822332511153139
1foundations-of-cybersecurity202305212024113054.82790544Information was well organized, easy to learn, and study. with frequent note writing, and some breaks . You can learn a good brief summary of what's to come, and what to research more in the future.551098822332511153139
2foundations-of-cybersecurity202305152024113054.82790541For a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses. I'm eager to move on to the other courses now.551098822332511153139
3foundations-of-cybersecurity202401092024113054.82790532I think this is a great start for anyone who is starting from absolute zero. I think that since I've been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher!551098822332511153139
4foundations-of-cybersecurity202305192024113054.82790524Surprised by the quality of this course. repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable.551098822332511153139
5foundations-of-cybersecurity202310282024113054.82790521! Cybersecurity is a critical and multifaceted field that involves protecting computer systems, networks, and data from various digital threats. Here is a review of the foundations of cybersecurity:551098822332511153139
6foundations-of-cybersecurity202306172024113014.82790518In the certificate , why am i not getting my name printed ? Instead "Coursera Learner"?551098822332511153139
7foundations-of-cybersecurity202306102024113054.8279059Dear Instructors of the Foundations of Cybersecurity Course at Google,I wanted to take a moment to express my deepest gratitude for the incredible learning experience you provided throughout the course. Your expertise, dedication, and passion for cybersecurity have truly made a lasting impact on my journey in this field.Firstly, allow me to introduce myself. My name is [Your Name], and I embarked on this course with a strong desire to deepen my understanding of cybersecurity and acquire the necessary skills to contribute meaningfully to the industry. As a lifelong learner, I am always seeking opportunities to expand my knowledge and stay updated with the latest developments in technology. This course seemed like the perfect fit to enhance my cybersecurity expertise.I chose to take this course because I firmly believe that cybersecurity is a critical aspect of our increasingly digital world. With the growing threats and vulnerabilities that organizations and individuals face, I wanted to equip myself with the knowledge and skills to make a tangible difference in securing digital systems and protecting sensitive information. The Foundations of Cybersecurity course seemed like the ideal starting point to build a strong foundation in this field.I am pleased to share that the course has exceeded my expectations in every way. From the very beginning, the course structure, content, and delivery were impeccable. The way you organized the modules and topics ensured a smooth learning journey, allowing me to grasp the fundamental concepts before diving into more advanced areas.What I truly loved about the course was the emphasis on practicality. The hands-on labs and simulations were invaluable in reinforcing the theoretical knowledge and providing a real-world perspective. Being able to apply the concepts in a practical setting not only enhanced my technical skills but also instilled confidence in my ability to tackle real-world cybersecurity challenges.Furthermore, the breadth of topics covered in the course was remarkable. From threat analysis to network security, encryption, incident response, and compliance, every aspect was explored in depth, providing a comprehensive understanding of the cybersecurity landscape. I appreciated the balance between theory and practical applications, as it allowed me to develop a holistic understanding of the subject matter.Your dedication as instructors was evident throughout the course. The quality of the course materials, including the informative videos, interactive quizzes, and additional readings, showcased the meticulous effort put into curating the content. Your ability to simplify complex concepts and communicate them effectively is commendable.The course has had a significant impact on my professional growth. Not only have I gained a solid understanding of cybersecurity fundamentals, but I have also acquired practical skills that I can immediately apply in real-world scenarios. The course has expanded my career prospects and opened doors to exciting opportunities in the cybersecurity field.I am truly grateful for the knowledge and insights I gained from this course. Your guidance and expertise have played a crucial role in shaping my cybersecurity journey. The impact you have made on my professional development is immeasurable.Thank you once again for your dedication, passion, and commitment to providing an exceptional learning experience. I am proud to have been a student in the Foundations of Cybersecurity course at Google, and I look forward to continuing my cybersecurity journey with the skills and knowledge I have acquired.With utmost appreciation,Jalal Saleem551098822332511153139
8foundations-of-cybersecurity202305202024113034.8279059The questions on the quizzes were often meaningless and the multiple choice answers were unbelievably vague and ill-defined to the point where no answer was entirely correct.551098822332511153139
9foundations-of-cybersecurity202306142024113054.8279058The instructors for the course did an amazing job at presenting all of the information to us! The course is informative and definitely will expand your knowledge specifically over Cybersecurity.551098822332511153139
course_nameposted_datecrawling_dateRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leaners
149000java-programming-arrays-lists-data202009162024113014.731680Not good515160710181071441
149001introduction-to-applied-cryptography202309052024113054.6660This course by Professor Keith Martin is a fast-paced introduction to the basics of cryptography and six important classes of applications.Even though the course is aimed at beginners, it contains a lot of material (e.g., on mobile telephony) that will be of interest to advanced students as well.What I appreciated most about the course was the broad perspective and the relaxed manner in which serious knowledge was taught.99693283454
149002introduction-to-applied-cryptography202305162024113054.6660Thank you so much during this 4 weeks, i gained more knowledges about cryptography in this course. This course really help me to learn in flexiblw time.99693283454
149003introduction-to-applied-cryptography202305162024113054.6660very useful and informative course.99693283454
149004introduction-to-applied-cryptography202309032024113054.6660Challenging and very enratainent.99693283454
149005introduction-to-applied-cryptography202311072024113054.6660very good education.99693283454
149006introduction-to-applied-cryptography202411122024113054.6660good to learn99693283454
149007introduction-to-applied-cryptography202311062024113054.6660It is Good99693283454
149008introduction-to-applied-cryptography202405172024113054.6660excellent99693283454
149009introduction-to-applied-cryptography202309292024113054.6660OK99693283454